Semantic Knowledge Management System (SKMS)

In her book, Ontology Pipeline: A framework for building knowledge infrastructures, author Jessica Talisman describes the notion of a "semantic knowledge management system" (SKMS).

Paraphrasing, a semantic knowledge management system is an organized structure of concepts, definitions, and relationships that lets both people and machines interpret information consistently and act on that information with confidence.

In the past, humans would "dip" into the well of knowledge.  Now, because of artificial intelligence, both humans and machines will be "dipping" into that well of knowledge.

This semantic knowledge will reconfigure the economics of human memory. This semantic knowledge will change business models and products.  Artificial intelligence is going to change every enterprise, large and small. Accountants will change from being "data janitors" and "spreadsheet monkeys" to being the curators and stewards of that semantic knowledge, I believe.

The future of accounting, auditing, and analysis is not artificial intelligence taking over. That simply will not happen.  The future will be the skills, knowledge, and judgement of accountants, auditors, and analysts being augmented and supplemented by artificial intelligence.

When you hire a new employee, do you trust that employee right off the bat? No. Why would you trust an artificial intelligence application?  Trust has to be earned.  "Confidence" is earned over time. Flip the artificial intelligence equation around and all this becomes clear. What skills, knowledge, and judgement does artificial intelligence bring to the table?  Just as you learn the capabilities of a new employee over time; so to you will learn the capabilities of artificial intelligence over time.

The key to using artificial intelligence effectively is not the artificial intelligence.  The key is the skills, knowledge, and judgement that humans curate and steward; the stuff in that semantic knowledge management system.  As Talisman puts it in her book, rules help minimize the "probabilistic fog" of LLMs.

Properly configured and fed with the proper skills, knowledge, and judgment; artificial intelligence can be a very helpful tool. Artificial intelligence, like any other software, does not work by "magic". To think that or to not understand how artificial intelligence works is a recipe for disaster.

In her book, Talisman summarizes the pieces of what she calls this semantic knowledge management system (SKMS).  For the most part, I buy into Talisman's approach.  I have also been influenced by Nicolas Figay's Inhabiting Blabel, Roy Roebuck's General Endeavor Management, global open industry standards, Dave McComb's perspectives on The Future of Accounting, and a few other inputs. My personal approach is AI-first, digital-first, graph-first. After extensive analysis I have synthesized everything into the following components of a semantic knowledge management system (SKMS) as applied to systems  where (a) there is no tolerance for error, (b) explain ability and traceability/trackability are important, and (c) business professionals are in charge of and operate these systems, not IT people (e.g. everything has to be easy to use). 

This is what I see grounded in Talisman's notion that we need to learn what librarians have already learned about knowledge management. The key driving use case is to minimize epistemic risk, keep risk as low as possible. Note that what you see below is an INVENTORY that needs further synthesis to make it simpler and remove duplication.
  • Controlled Vocabularies: As I have pointed out before, computers are dumb beasts. You have to give them a chance to succeed.  Part of that is agreeing on meaning and turning that meaning into understanding. This needs to be done as a collective, usually across several or many different connectives inside and outside your enterprise. A common controlled vocabulary in both human readable and machine interpretable form is an absolutely necessary starting point. Governance is critical. Provenance is critical. Traceability is critical. Explainability is critical.
  • Metadata Standards: You do not live in an island. Not your team, not your department, not your  location, not your enterprise; none are islands.  Metadata for structuring, describing, and administering your semantic knowledge is critically important.  Intuitively I believe that upper level or top level ontologies are important but I also understand the current risks as these get worked out.
  • Taxonomy: A taxonomy takes a controlled vocabulary and turns it into a hierarchical structure or set of hierarchical structures.  This is about "is-a" or "general-special" or "type-subtype" and other such connections between controlled vocabulary concepts.
  • Thesaurus: A thesaurus builds on the foundation of a taxonomy and reduces ambiguity and makes the controlled vocabulary easier to use effectively.
  • Ontology: An ontology further expands on controlled vocabularies, taxonomies, thesaurus; but also introduces new capabilities. One definition of an ontology is, "an ontology is a domain model that results from a process of ontological analysis and is a semantic contract that tells the world what your worldview is about that domain and what is assumed to exist and with which nature/definition, and what are the state of affairs deemed acceptable in that domain". To simplify understanding "ontology" and to overcome limitations, I personally tend to prefer the notion of a theory.
  • Theory: A theory provides a deliberate, purposeful, carefully vetted, explicit, formal unambiguous explanation of what things makes up a system, connections between those things, conditions those things and relations must satisfy, and the goals and objectives desired by the stakeholders of the system the theory is describing. (Theory = terms + organization + schemas + business rules + process rules + inference rules + instances)
  • Model: A model is an intentional abstraction of reality: a simplified, structured way of describing a system so people can understand it, communicate about it, and solve problems together. It is not the real-world thing itself, but a useful approximation that captures the essential elements, relationships, and patterns of a subject domain. A model is an abstraction of reality according to a certain conceptualization.  Leveraging the Atomic Design Methodology can make things easier for business professionals to use. Note that fundamentally, the information that enterprises work  with tends to be multidimensional in nature. XBRL provides a multidimensional semantic information model.  The W3C Data Cube Vocabulary is a coordinate system for data.
  • Structured Information: This system is about information, not data.  If you do not understand the difference, then big mistakes will be made. This is about managing professional knowledge some of  which exists within an enterprise but other knowledge exists outside the enterprise such as compliance reporting rules and standards. SHACL seems excellent for constraining structures.
  • Structured Information Report Writer: Information, in the form of structures, can be "reported" using discrete stable units of information. See this example. Think semantic oriented "report writer" that provides a graph of information that can be projected into human readable form using a global standard model.
  • Knowledge Graph: The general notion of a knowledge graph is a good idea, independent of any specific technical implementation. But the W3C semantic web is the gold standard and everything you do must be reconcilable to that.
  • Intelligence Engine: Working on all that knowledge is a neuro-semantic oriented intelligence engine. DATALOG at it's core for safety and reliability. Note that a lot of information that accountants, auditors, and analysts work with is numeric with rich relationships.  XBRL Formula provides capabilities for processing this information above and  beyond with the W3C provides.
  • Adaptive Technology Stack: Enterprises build a modular, adaptable, technology stack that supports multiple technology approaches, this is a "layer cake" which supports digital distributed ledgers, crypto currency, etc.
  • Operations Redesign: Building on a foundation that is unstable or fundamentally wrong is a recipe for disaster.  Retrofitting artificial intelligence on top of legacy workflows is simply not going to work. Accounting & Audit by Design (A&AD) Framework explains my thinking well. Fundamentally, the objective is to maximize the potential of artificial intelligence.
  • Human-AI Teaming: Roles, teams and organizational structures are designed to enable continuous collaboration between people and AI systems, defining what human contribution looks like at the frontier.
  • New Value Creation: Think knowledge as a product.  Think along the lines of defensible knowledge and experience moats.  Every AI-First, Digital-First, Graph-First enterprise, regardless of sector, must decide how to position intelligence integration in the market. From AI as a product feature to AI as invisible infrastructure, positioning choice determines what customers pay for, where value accrues and how the business competes.
Obviously, this needs further synthesis.  What I am seeing is a hybrid. Systems that are "general" like a database that can be used for anything is not what business professionals need.  What business professionals need is a "specialized" system that sits on top of a "general" semantic knowledge management system.  Accountants, auditors, and analysts need specialized work systems build on top of more general tools; not general tools.  More to come.

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